PD6629 Sample Summary

## `summarise()` has grouped output by 'patient', 'age_at_sample_exact', 'age_at_sample', 'DOB', 'DATE_OF_DIAGNOSIS'. You can override using the `.groups` argument.
## Joining, by = "PDID"
patient ID age_at_sample_exact cell_type phase BaitLabel
3 PD6629 PD6629dc 55.05270 PB whole Recapture PD6629dc
4 PD6629 PD6629dd 58.83094 PB Gran Recapture PD6629dd
1 PD6629 COLONY60 60.26831 BFU-E-Colony Colony NA
5 PD6629 PD6629de 60.26831 PB Gran Recapture PD6629de
2 PD6629 COLONY62 62.28063 BFU-E-Colony Colony NA
6 PD6629 PD6629df 63.41136 PB Gran Recapture PD6629df
7 PD6629 PD6629dg 64.73374 PB Gran Recapture PD6629dg
8 PD6629 PD6629dh 65.86448 PB Gran Recapture PD6629dh

Tree

tree=plot_basic_tree(PD$pdx,label = PD$patient,style="classic")

Expanded Tree with Node Labels

The nodes in this plot can be cross-referenced with nodes specified in subsequent results. The plot also serves to give an idea of what the topology at the top of the tree looks like.

tree=plot_basic_tree(expand_short_branches(PD$pdx,prop = 0.1),label = PD$patient,style="classic")
node_labels(tree)

Timing of driver mutations (using Model = poisson_tree )

Note that the different colours on the tree indicate the separately fitted mutation rate clades.

Driver Specific Mutation Rates & Telomere Lengths by Colony & Timepoint

## 
## Random-Effects Model (k = 1; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   0.0000   -0.0000    4.0000      -Inf   16.0000   
## 
## tau^2 (estimated amount of total heterogeneity): 0
## tau (square root of estimated tau^2 value):      0
## I^2 (total heterogeneity / total variability):   0.00%
## H^2 (total variability / sampling variability):  1.00
## 
## Test for Heterogeneity:
## Q(df = 0) = 0.0000, p-val = 1.0000
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  17.8576  1.0236  17.4466  <.0001  15.8514  19.8637  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
node driver status child_count type colony_count mean_lambda_rescaled correction sd_rescaled lb_rescaled ub_rescaled median_rescaled p_lt_wt
-1 WT 1 -1 local 1 17.85758 1.014746 1.0476692 15.918788 19.99907 17.82518 NA
63 DNMT3A 1 56 local 21 18.29717 1.014746 0.3236644 17.686498 18.95402 18.28683 0.346950
68 JAK2:DNMT3A 1 35 local 25 18.07328 1.014746 0.5992949 16.962819 19.31095 18.05629 0.426150
59 DNMT3A 0 1 local 1 20.30765 1.014746 2.8738357 14.401820 26.71867 20.04911 0.196000
58 CBL 0 1 local 1 14.78254 1.014746 2.6064727 9.282933 20.53121 14.90475 0.883925
88 9pUPD:JAK2:DNMT3A 0 2 local 2 19.01943 1.014746 1.3174011 16.620660 21.84153 18.94668 0.250200
76 TET2:JAK2:DNMT3A 1 8 local 8 17.20549 1.014746 0.9877071 15.345362 19.23166 17.17896 0.670825

Driver Acquisition Timeline

All ages are in terms of post conception years. The vertical red lines denote when colonies were sampled and blue lines when targeted follow up samples were taken.

patient node driver child_count lower_median upper_median lower_lb95 lower_ub95 upper_lb95 upper_ub95 N group age_at_diagnosis_pcy max_age_at_sample min_age_at_sample
PD6629 63 DNMT3A 56 0.0087466 7.263952 0.0061894 0.0222232 5.937797 8.782189 8 DNMT3A 54.8063 66.59274 55.78097
PD6629 68 JAK2 35 14.2541270 29.728295 12.6154093 15.9897441 27.776801 31.722219 8 JAK2 54.8063 66.59274 55.78097
PD6629 76 TET2 8 36.1591058 46.033123 34.3227129 37.9689969 44.237803 47.683728 8 TET2 54.8063 66.59274 55.78097
PD6629 88 9pUPD 2 32.8006950 46.316776 30.8979890 34.6854530 44.430877 48.241853 8 9pUPD 54.8063 66.59274 55.78097

Copy Number Variation and Timing

Summary of LOH timing inference

## Timings using the Clade Specific Rates
label node het.sensitivity chr start end nhet nhom mean_loh_event lower_loh_event upper_loh_event t_before_end t_before_end_lower t_before_end_upper kb count_in_bin count_se pmut pmut_se xmean xse_mean xsd x2.5. x50. x97.5. xn_eff xRhat lmean lse_mean patient driver3 child_count
LOH_9pUPD 88 0.9103 9 14690 33393006 0 2 43.59 37.76 46.24 2.736 0.08701 8.561 33300000 6654 81.57 0.01453 0.0001782 0.7977 0.001115 0.17 0.367 0.8425 0.9936 23273 1 3.42 0.0003039 PD6629 9pUPD:JAK2:DNMT3A 2

Duplications?

VAF Distribution of Targeted Follow Up Samples

Here we exclude all local CNAs and depict as color VAF plots